Concept Of Membership Research Articles

Non-elastic time series fuzzy clustering for efficient analysis of industrial data sets

In line with the green transition and digitalization trends, there is an increasing need for effective performance and health monitoring of production machines. Machine operation footprints resonate in quantities such as supply current and pneumatic line pressure, providing valuable insights when analyzed. Examining these low-level signals allows for monitoring and identification of changes in machine operation over time. A critical step in analyzing machine data is clustering, requiring an effective method for calculating time series cluster centroids. This paper introduces Error in Aligned series (ERAL), a novel method that generates a time series centroid that distills the fundamental shape of the underlying datasets. ERAL employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods. Our analysis shows that existing methods can create artificial spikes and plateaus, which ERAL mitigates; it remains faithful to the original data shape. Additionally, ERAL offers improvements in computational efficiency and prototype quality. We evaluate the method against Dynamic Time Warping (DTW)-based methods across various datasets, and apply it to a time series clustering task using a real-world industrial dataset. In combination with a fuzzy clustering algorithm, ERAL generates visually convincing clusters. By leveraging fuzzy membership concepts, it achieves robust and adaptable clustering outcomes that reflect real-world data complexity and ambiguity.

International Memberships and Their Contribution to the Development of Additional State Capacities of Bosnia and Herzegovina: Membership in the Council of Europe

The membership of Bosnia and Herzegovina (BiH) in the Council of Europe, in a broader theoretical sense, is an example of a somewhat neglected aspect in the relations between states and international organizations — the effect that international memberships, with a company in rights and obligations, have on achieving internal political consensus and building additional state capacities. This is particularly important for small, vulnerable, and highly decentralized states like Bosnia and Herzegovina. Although the concept of membership is neither demanding nor exclusive like that of the European Union, BiH waged a fierce diplomatic and political battle for this membership – by fulfilling pre-accession conditions (adopting the Election Law of BiH) and through intensive diplomatic lobbying, along with the obligation to meet a whole series of post-admission conditions. More than two decades of membership have provided examples of building additional state capacities – the imperative for constitutional changes, the upgrading of judicial and diplomatic-bureaucratic infrastructure through the formation of inter-ministerial groups, innovative institutional solutions, introduction of new functions or institutions; projecting one's political values in the context of current problems and issues that the international organization and its membership address, and opening new opportunities for international careers for officials, diplomats, or citizens of BiH.

Enhancing class imbalance solutions: A projection-based fuzzy LS-TSVM approach

Class imbalance and noise present significant challenges in numerous real-world classification tasks. The prevalence of an uneven distribution of samples typically results in a bias towards the majority class in Support Vector Machine (SVM) classifiers, compounded by the often inherent noise within these samples. Addressing both class imbalance and noise, we introduce two fuzzy-based methodologies. The first method employs intuitionistic fuzzy membership, resulting in the development of the Robust Energy-based Intuitionistic Fuzzy Least Squares Twin Support Vector Machine (IF-RELSTSVM), a model specifically designed for class imbalance learning. The IF-RELSTSVM model is distinguished by its use of intuitionistic fuzzy scores for both classes, significantly attenuating the detrimental effects of noise and outliers. A distinctive attribute of IF-RELSTSVM is its proficiency in processing noisy data points, whether proximate to or distant from the hyperplane. Additionally, we introduce a novel concept of hyperplane-based fuzzy membership, calculating fuzzy memberships through a projection-based approach. This foundation supports the formulation of a Robust Energy-based Fuzzy Least Squares Twin Support Vector Machine (F-RELSTSVM), also aimed at class imbalance learning. The efficacy of the proposed IF-RELSTSVM and F-RELSTSVM algorithms is rigorously evaluated across several benchmark and synthetic datasets, employing the Area Under the ROC Curve (AUC) as a performance metric. Experimental findings indicate that these algorithms surpass baseline models in the majority of datasets tested. Statistical analyses further validate the significance of our proposed methods, demonstrating their suitability for application in environments characterized by noise and class imbalance. A case study in credit card fraud detection showcases the F-RELSTSVM algorithm achieving an impressive average AUC of 90.84%, thereby outperforming comparable algorithms and highlighting the practical applicability of our approaches in tackling challenging datasets.

The Civil Law Status of Social Movements

The need to improve legislation in the field of regulating relations involving public associations has been overdue for a long time. The solution of problematic issues will contribute to a more effective and unhindered exercise of the right to association. Currently, social movements and public organizations do not belong to the same organizational and legal form of non-profit organizations. Nevertheless, there is no consensus in the doctrine of civil law as to how justified this approach is. The objectives of the creation are named as the peculiarities of the social movement, as well as the fact that, unlike the public organization, the participants of the social movement do not acquire the right of membership. They just support the objectives of its creation and operation. At the same time, the analysis of the charters of many social movements shows that their participants, as a rule, fix their participation in the social movement by submitting individual applications, going through the registration and accounting procedures, which eliminates the difference with the status of members in a public organization. In addition, the concepts of participation and membership in civil legislation are usually identified, while in the legislation on public associations their status differs depending on whether there is a procedure for formalizing participation or not. It seems that the lack of certainty in the legal status of social movements as legal entities is connected not only with the revealed contradictions in the laws that regulate it, but also with the fact that the civil law doctrine has not developed a unified approach to determining the category of organizational and legal form of legal entities. The issue concerning sufficiency of highlighting the specifics of the creation objectives for the formation of certain types of legal entities still needs resolution.

Sparsity Fuzzy C-Means Clustering With Principal Component Analysis Embedding

The clustering method has been widely used in data mining, pattern recognition, and image identification. Fuzzy c-means (FCM) is a soft clustering method that introduces the concept of membership. In this method, the fuzzy membership matrix is obtained by calculating the distance between data points in the original space. However, these methods may yield suboptimal results owing to the influence of redundant features. Moreover, FCM is always sensitive to noise points and heavily subject to outliers. In this paper, we propose a method called sparsity FCM clustering with principal component analysis embedding (P_SFCM). We simultaneously conduct principal component analysis (PCA) and membership learning, and then add an additional weighting factor for each data point. The goal of this operation is to identify the noise or outliers. Overall, the benefit of our framework is that it retains most of the information in the subspace while improving the robustness of the noise. In this paper, we employ an iterative optimization algorithm to efficiently solve our model. To verify the reliability of the proposed method, we conduct a convergence analysis, noise robustness analysis, and multi-cluster experiments. Furthermore, comparative experiments are conducted on both synthetic and real benchmark datasets. The experimental results show that the P_SFCM is competitive with comparable methods.

A Knowledge Sharing and Individually Guided Evolutionary Algorithm for Multi-Task Optimization Problems

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm. It focuses on solving multiple optimization tasks concurrently while improving optimization performance by utilizing similarities among tasks and historical optimization knowledge. To ensure its high performance, it is important to choose proper individuals for each task. Most MTO algorithms limit each individual to one task, which weakens the effects of information exchange. To improve the efficiency of knowledge transfer and choose more suitable individuals to learn from other tasks, this work proposes a general MTO framework named individually guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones, and each individual is fully explored to learn experience from the execution of other tasks. By using the concept of skill membership, individuals with higher solving ability are selected. Besides, to further improve the effect of knowledge transfer, only inferior individuals are selected to learn from other tasks at each generation. The significant advantage of IMTO over the multifactorial evolutionary framework and baseline solvers is verified via a series of benchmark studies.

Secure distributed power trading protocol for networked microgrids based on blockchain and elliptic curve cryptography

AbstractThe definition of an electricity trading protocol among microgrids is not an easy task to perform due to the grid's increasing requirements. In fact, a trading process must provide security and especially privacy of the buyer and seller identities as well as prices. Also, guaranteeing a financial profit to trading parties is of great importnace. Moreover, matching the full distributed paradigm is required since it will be executed in a distributed system. All these constraints must be satisfied with consideration to the computational costs especially data storage. In this paper, an electricity trading protocol is introduced to ensure: (1) the security and privacy of buyers, sellers, and sales information, (2) the trading process distribution, and (3) the minimisation of the stored data. The protocol builds upon the blockchain technology and the Elliptic Curve Cryptography. To achieve the protocol goals, a new architecture based on a concept of membership of microgrids to families and groups of families is defined. The proposed protocol is implemented using Java programming langauge. An honest‐but‐curious‐adversary security model is applied to a formal case study to prove security of the protocol.

Refugees, membership, and state system legitimacy

In the literature on refugeehood in political theory, there has been a recent turn towards what have been called “state system legitimacy” views. These views derive an account of states’ obligations to refugees from a broader picture of the conditions for international legitimacy. This paper seeks to develop the state system legitimacy view of refugeehood by subjecting the most developed version of it—the account developed by David Owen—to critical scrutiny. We diagnose an ambiguity in Owen’s theory of refugeehood, in the concept of political membership, and unpack the implications of this ambiguity for state system legitimacy views. First, we reconstruct the key aspects of Owen’s account of refugeehood and show how it represents an advance over competing theories. Then we discuss the methodological underpinnings of Owen’s account, showing the constraints and opportunities faced by state system legitimacy views. Next, we raise some problems for the conceptual distinctions that Owen develops between different types of refugee protection: asylum, sanctuary, and refuge. The underlying feature that leads to these problems is an ambiguity in the concept of political membership, which is at the core of Owen’s view of refugeehood. Finally, we distinguish two interpretations of political membership in the institution of refugeehood and chart out some possible ways forward for state system legitimacy views. The critique developed here is a sympathetic one, aimed at the further development of state system legitimacy views.

ESCP President's report.

ESCP President's report.

Ties that bind and unbind: charting the boundaries of European Union citizenship

ABSTRACT Acquiring or losing European Union (EU) citizenship is contingent on the possession of citizenship of one of the Member States. Since the early 1990s, scholars have debated the unique character of EU citizenship. Remarkably, these debates have paid scarcely any attention to how EU citizenship is (un)recognised through the different conceptions of membership in the 27 countries that make up its boundaries. This article argues that understanding the substance of EU citizenship requires a look into the different domains of citizenship laws in each of the Member States. We present a novel conceptual framework for studying citizenship regimes through four types of citizen-state links: lineage, territory, sponsorship and merit. We find that the disparity among the Member States in who is (un)seen as an EU citizen results from the different ways in which the four types of state-citizen links are articulated in the rules for citizenship acquisition and loss.

Protecting Trafficked Persons through Refugee Protection

This paper critically engages with the long-term protection of trafficked persons. In particular, it assesses whether, and the conditions under which, trafficked persons can be considered as refugees under Article 1A of the Geneva Refugee Convention. The importance of international refugee law in this context is highlighted both by the number of trafficked persons seeking international protection and by its suitability to overcome the shortcomings of existing protection provisions in anti-trafficking instruments, which remain discretionary, conditional, and limited in scope. The paper begins by discussing the relevance of refugee protection for trafficked persons. It then applies the various components of the refugee definition to trafficked persons, focusing on the concepts of persecution and membership of a particular social group. Within these, it focuses on aspects of the debate that are currently missing from the broader literature. This includes the question of whether trafficking qua trafficking meets the threshold of persecution and the value of developments in international law in that regard, the merit of using race as a convention ground in cases of trafficked persons, and the contribution of legislative developments recognizing former victims of trafficked persons as members of a particular social group. It then concludes by highlighting the benefits of refugee protection for trafficked persons and indicating the challenges on the road to that protection.

A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images

Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer’s disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%.

Real-time deep learning-based market demand forecasting and monitoring

• Develop a CNN algorithm consists of two parts of feature extraction and classification in one model which makes it a unique and practical model in the area. • Develop a model to find the highest effective features for the market demand prediction • Training the CNN hyper parameters using a heuristic model based on PSO . • Considering the very complexity of the market demand in the new competitive environment using a novel fuzzy-based PSO • Use the fuzzy membership concept with aid of PSO to change its parameters dynamically rather than considering constant values. Abstract The concept of market demand forecasting is a very precious and significant problem in the industry without which the generator companies would not be able to give a suitable offer and may experience big losses. This research introduces a novel deep learning based on convolutional neural networks (CNN) to learn the market behavior in details. The model comprises a weight update mechanism in the backpropagation process of CNN to enhance its performance. Moreover, a fuzzy particle swarm optimization (FPSO) based algorithm is used to enhance the CNN performance and find the most accurate predictions. The proposed FPSO would utilize the fuzzy set theory and membership function to update the weighting coefficients . To validate this proposal, real market demand data are used as the benchmark and the results are discussed in the paper. The simulation results prove the suitable performance of the proposed CNN-FPSO model.

‘It’s Just Safer When I Don’t Go There’: Trans People’s Locker Room Membership and Participation in Physical Activity

ABSTRACT Building on findings demonstrating that transgender and gender non-binary (trans) people’s participation in physical activity is impacted by their experiences in locker rooms, this study examines how trans people navigate transphobia and cissexism in locker rooms. I consider the concepts of membership and belonging to illuminate how locker room access impacts trans people’s participation in the public sphere. Drawing on interviews with trans people, this study suggests that—because of iterative encounters of surveillance, scrutiny, harassment, and violence—trans people regularly adopt strategies for navigating locker rooms as nonmembers. To access locker rooms despite a denial of membership, trans people employ various strategies to minimize the visibility of their transness and/or gender non-conformity through managing the perceived threat their gender expression poses to a “cisgendered reality.” Strategies such as hurrying, avoiding nudity and eye contact, and recruiting ally support function to facilitate locker room access by minimizing attention to trans nonmembership. The findings of this study suggest that whiteness broadens the availability of strategies for navigating locker rooms. This study offers insight into how people respond to stigma, evade surveillance and exclusion, and access public space despite a denial of membership.

TOWARDS GRADUAL INTEGRATION OF THE WESTERN BALKANS INTO THE EUROPEAN UNION: THE CASE OF SERBIA

This paper examined whether the revised enlargement methodology represented another step in the direction of gradual EU membership of the Western Balkans. The research problem was based on considerations that the EU enlargement domain has for years been characterized by political stagnation. The research aimed to show that a differentiated accession approach might have been beneficial for Serbia, as a front-runner membership candidate. In terms of methods, the author analyzes the concepts and the applicability of the ‘three-tiered accession’ and ‘staged accession’, having in mind the recently altered enlargement methodology. The author argues that the gradual integration would have been beneficial for two reasons: 1) validating the candidates’ already achieved progress, offering tangible incentives, and potentially preventing the democratic backsliding; 2) taking the pre-scheduled accession dates off the table. Considering that the EU is unlikely to expand further, the author concludes that the gradual membership concept might apply solely to the WB, while also reassuring the Member States regarding the primacy of individual merits and excluding the speedy accessions proposals.

The Relationship Between (sub)national Identity, Citizenship Conceptions, and Perceived Ethnic Threat in Flanders and Wallonia for the Period 1995–2020: A Measurement Invariance Testing Strategy

This article examines the relationship between (sub)national identity and attitudes towards immigrants in the multinational context of Belgium. We extend our previous studies by analysing a longer time period (1995–2020) and by making a strong case for the idea that measurement invariance testing and theoretical meaningfulness are closely intertwined. To examine whether and how the relationship between (sub)national identity and perceived ethnic threat has changed over time and between regions, we first test for metric invariance of the latent concepts. Using data from the Belgian National Election Studies, we illustrate that evaluating invariance of measurements is a necessary condition for comparative research, but also that measurement equivalence testing should be considered as an empirical guide showing researchers where substantial conclusions should potentially be revisited and theoretical validity rethought. Next, we verify whether the relationship between (sub)national identity and perceptions of ethnic threat across subnational units can be attributed to different conceptions of community membership -in terms of ethnic and/or civic citizenship conceptions- in Flanders and Wallonia. While we expected that a strong identification with Flanders would primarily be related to an ethnic citizenship representation, and as a result, stronger feelings of threat towards immigrants; we expected that a strong identification with Wallonia would primarily be related to a civic representation of the nation and therefore lower feelings of threat. Thanks to our thorough invariance testing strategy, the conceptualisation and measurement of (sub)national identity had to be adjusted in Wallonia, and the hypotheses had to be qualified.

Freedom to Define Membership in a Mutual Insurance Company

A mutual insurance society is one of the basic forms of conducting insurance activity. It is characterized by a very wide range of options which its founders and subsequently entitled members have in order to choose the organizational and systemic model of operation, to change it in the course of business, to define the concept of membership, to create various categories of members and provide them with different rights and duties, to determine the powers of statutory bodies, and, above all, to apply the method of mutuality. The Insurance and Reinsurance Activity Act regulates the basic legal framework of mutual companies, while referring quite a number of issues to the Polish Commercial Partnerships and Companies Code. This does not alter the fundamental principle on which the company's activity is based, namely that its articles of association play an extremely important role, which is much greater than in the case of public limited liability companies, and that members of a mutual insurance society enjoy considerable freedom to conduct business and categorize its members, which is unparalleled for other legal forms of business activity.

Birds of a feather speak together: Understanding the different dialects of animals can help to decipher their communication.

As humans, many animal species that communicate via vocalization show a wide range of accents and dialects driven by environmental and social factors.

Cooperation-based concept formation in male bottlenose dolphins

In Shark Bay, Western Australia, male bottlenose dolphins form a complex nested alliance hierarchy. At the first level, pairs or trios of unrelated males cooperate to herd individual females. Multiple first-order alliances cooperate in teams (second-order alliances) in the pursuit and defence of females, and multiple teams also work together (third-order alliances). Yet it remains unknown how dolphins classify these nested alliance relationships. We use 30 years of behavioural data combined with 40 contemporary sound playback experiments to 14 allied males, recording responses with drone-mounted video and a hydrophone array. We show that males form a first-person social concept of cooperative team membership at the second-order alliance level, independently of first-order alliance history and current relationship strength across all three alliance levels. Such associative concepts develop through experience and likely played an important role in the cooperative behaviour of early humans. These results provide evidence that cooperation-based concepts are not unique to humans, occurring in other animal societies with extensive cooperation between non-kin.

Evolving Fuzzy Clustering Approach: An Epoch Clustering That Enables Heuristic Postpruning

Clustering is an unsupervised machine learning method that is used both individually and as a part of the preprocessing stage for the supervised machine learning methods. Due to its unsupervised nature, clustering results have less accuracy compared to the supervised learning. This article aims to introduce a new perspective in clustering by defining an approach for data pruning. The method also enables clustering using multiple sets of prototypes instead of only one set to improve clustering accuracy. Consequently, this approach has the potential to be used independently or as a part of a preprocessing to prepare purified data for the training step of a supervised learning approach. An evolving fuzzy clustering approach (EFCA) utilizes the fuzzy membership concept to breakdown clustering in epochs instead of running the clustering on all data at once. In some cases, for supervised learning, we rather have a smaller subset of highly accurate labeled data instead of a dataset with less accurate labels. The EFCA's “epoch cut” enables postpruning ability to eliminate obscure data points, which results in more clustering accuracy. The EFCA has been applied to a set of eight multivariate and ten time-series datasets, and for example, after deploying epoch cut and eliminating obscure data (20% of data) by automatic postpruning, it achieved 100% accuracy for the rest 80% Iris data.